A Survey of Deep Meta-Learning

• URL: http://arxiv.org/abs/2010.03522v2
• Date: Wed, 21 Apr 2021 13:33:40 GMT
• Title: A Survey of Deep Meta-Learning
• Authors: Mike Huisman and Jan N. van Rijn and Aske Plaat
• Abstract summary: Deep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is limited. Deep Meta-Learning is one approach to address this issue, by enabling the network to learn how to learn.
• Score: 1.2891210250935143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is limited. Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. The field of Deep Meta-Learning advances at great speed, but lacks a unified, in-depth overview of current techniques. With this work, we aim to bridge this gap. After providing the reader with a theoretical foundation, we investigate and summarize key methods, which are categorized into i)~metric-, ii)~model-, and iii)~optimization-based techniques. In addition, we identify the main open challenges, such as performance evaluations on heterogeneous benchmarks, and reduction of the computational costs of meta-learning.

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